Virtual foreman dispatch planning system

Abstract

The present invention provides a virtual foreman dispatch planning system installed in a host in a factory, including: a knowledge graph unit, a matching unit and a recommendation unit. The knowledge graph unit has a first memory and a second memory which are connected with each other, and constructs and stores structural information including checking nodes, maintenance nodes and edges. The matching unit includes a neural network classifier that adopts semi-supervised learning method to retain original structural information, and downgrades the dimension of a continuous lantent space so that the continuous lantent space becomes a vector space, making nodes with more similar structures be closer in distance in the vector space. Through the K-Nearest Neighbor algorithm, the recommendation unit calculates the node of the maintenance record nearest to the vector space which is used as the dispatched manpower required for recommendation, so as to achieve the optimal dispatching effect.

What is claimed is: 
     
         1 . A virtual foreman dispatch planning system, installed in a host in a factory and comprising:
 a knowledge graph unit having a first memory and a second memory connected with each other, wherein the first memory stores information of components of each machine, checking items of said each machine and checking records of an operator, as checking nodes; the second memory stores information of said each machine and the components of said each machine and stores a maintenance record of the operator, as maintenance nodes; and each of the checking nodes and maintenance nodes are associated in order to be linearly connected and stored as edges, wherein if the checking items or maintenance items of a same component belong to different operators, said different operators are jointly connected to the same component to form structural information;   a matching unit connected with the knowledge graph unit and comprising at least one neural network classifier, wherein regarding the structural information of the checking nodes, the maintenance nodes and edges, the neural network classifier adopts a semi-supervised learning method to retain the structural information stored in the first memory and the second memory, and downgrade the dimension of the structural information to a continuous lantent space to serve as a vector space, making nodes with more similar structures closer to each other in distance in the vector space; and   a recommendation unit connected with the matching unit and comprising at least one microprocessor, wherein the recommendation unit adopts a K-nearest neighbor (KNN) algorithm to calculate similarity by calculating distances, finding neighbors and performing classification, provides a certain requested checking node or maintenance node, and searches for a nearest node in the vector space from the maintenance record as a recommended optimal dispatch.   
     
     
         2 . The virtual foreman dispatch planning system according to  claim 1 , wherein contents of the checking items and maintenance items stored in the first memory and the second memory come from the components of said each machine, and at least comprise a motor, heater, indicator light, material inlet and material outlet. 
     
     
         3 . The virtual foreman dispatch planning system according to  claim 1 , wherein a neural network classifier of the matching unit has an optimization area, and the optimization area optimizes a first-order similarity and second-order similarity through an optimization objective algorithm, wherein the first-order similarity is defined by referring nodes adjacent to a given node in the structural information as first-order neighbors; the second-order similarity is defined by referring nodes having a common first-order neighbor as second-order neighbors; and based on following equations of the optimization objective algorithm, vector spaces of nodes on the structural information belonging to the first-order neighbors or the second-order neighbors are closer to one another, in comparison with vector spaces of nodes on the structural information not belonging to the first-order neighbors or the second-order neighbors; 
       
         
           
             
               
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         wherein N 1  (vi) represents a set of vi first-order neighbors, P 1 (vi) represents distribution of non-vi first-order neighbors, and zi and zj represent embedding vectors of nodes vi and vj respectively. 
       
     
     
         4 . The virtual foreman dispatch planning system according to  claim 1 , wherein distances in the KNN algorithm of the recommendation unit are calculated by: providing a node to be evaluated, calculating distances between the node to be evaluated and each node in the structural information by using Euclidean distance, Manhattan distance and cosine of included angle respectively, so as to measure the dissimilarity between objects, wherein the Euclidean distance is used for relational data; and cosine of included angle is used to calculate similarities for text classification. 
     
     
         5 . The virtual foreman dispatch planning system according to  claim 1 , wherein the KNN algorithm of the recommendation unit selects several nearest nodes as neighbors of a node to be evaluated, and the KNN algorithm adopts cross-validation and empirical rules, wherein one part of calculated values is used as samples for a training set of the neural network classifier of the matching unit; another part of the calculated values is used as a testing set, and several nearest nodes are selected by the empirical rules; said several nearest nodes constantly are adjusted from the beginning till the end to optimize sample classification; when the sample classification is optimal, values of said nearest nodes are selected values; and distances between each of the samples in the entire training set and the node to be evaluated are calculated to select several nearest nodes as nearest neighbors. 
     
     
         6 . The virtual foreman dispatch planning system according to  claim 1 , wherein the classification in the KNN algorithm of the recommendation unit determines the category in which said nearest nodes shows up most often as a prediction category of a node to be evaluated; the classification in the KNN algorithm comprises comprehensive voting decision and weighting method, wherein the voting decision is defined by that the minority obeys the majority, and the category with most number of nodes in the neighbors of several nearest nodes is selected as the chosen category; and the weighted voting rule is to weight votes of the neighbors according to the magnitude of distance, and the closer the distance, the greater the weight.